In certain fields of work such as the development of printing technology, scanning technology, digital and film photography, and color correction in the movie film industry, there is very frequently a need to efficiently translate a color image that exists in one medium over to another medium in such a way that the colors of the image, as displayed by both mediums, do not appear to change. This is known as color translation accuracy. For instance, a developer of printing technology may want to translate an image that has been created with a graphical art image editing application or that has been scanned into a computer into an image consisting of ink on paper that most accurately resembles the monitor image. Or someone involved with the post production of movie film may want to copy an image that exists on film onto a computer monitor with the least amount of distortion, and then may want to convert that image back onto film.
The principle difficulty in accurately and efficiently translating a color image from one medium to another lies in the limits to the range of colors each medium is able to display. A color space, which is also known as a color gamut or simply gamut, is an ordered structure containing a broad range of colors arranged in a way that is analogous to physical space, i.e. multiple dimensions, each axis perpendicular to the others. For instance, RGB is the color space typically used by CRT's and contains red, green and blue axes, where individual points or pixels in this space are colors composed of levels of red, green and blue light. CMYK is another color space typically used by color printers that contains cyan, magenta, yellow, and black axes, where individual pixels in this space are colors composed of cyan, magenta, yellow, and block light levels. In the event that a color space associated with one medium may have greater or fewer colors than the color space associated with another medium, it becomes necessary for the color image translation process to correct or adjust the original images colors during the translation process in order for them to be displayed in another color space. Prior art color image translation methods tend to distort the displayed color contrast relationships, as between the original and translated color image, resulting in an image that does not exhibit the same colors or color contrast as the original.
A number of different prior art methods have been developed that attempt to accurately perform the color image translation process. One method employed was to simply map, as closely as possible, all of the colors in one color space, RGB for example, directly to the colors of another color space, CMYK for example. Due to the differences in RGB and CMYK display technologies (illumination vs. ink), it is not possible to construct a mapping scheme which will accurately display the original RGB colors in CMYK color space. Very often, the resulting translated image colors do not, to the human eye, resemble the colors of the original color image. Consequently, the colors of the resulting translated image may have to be manually adjusted until the color relationships in this image, are deemed an accurate translation according to human eyes and judgment. This is a very time consuming, difficult process, and so it would be desirable to have an efficient method that automates certain steps of the process in order to achieve a level of color image translation accuracy.
Prior art color image translation methods are known that translate a color image from a source medium to a target medium using a process that automatically accounts for some basic color image translation accuracy. These methods vary from each other with respect to how they handle the difference in the gamuts available to the source and target mediums. Different color gamuts can mean, for instance, that the source medium may be capable of displaying very light colors, but not dark colors, while the target medium, which would display the translated image, may only be capable of displaying dark colors and not light colors. These color image translation methods all utilize some form of an intermediate color space to permit the differences between the source and target medium's gamuts to be analyzed and identified so that only those colors outside the target mediums gamut need to be adjusted. Thus if the original image contains colors which cannot be displayed by the target medium, certain remedial steps must be taken to ensure that all colors in the original image can be displayed by the target medium while still achieving the most accurate result. This manual, remedial process is iterative and can take quite a lot of time.
Typically, image translation methods handle this problem by adjusting the colors of an original image in an intermediate color space until most or all of the colors exist in the range or gamut of the target medium. To adjust all colors of an image from one original gamut to another target gamut in an intermediate color space is to adjust every color with respect to one or more axes in the direction of the target gamut. For instance, using the RGB space, the modified image may be significantly greener than the original image.
As mentioned above, color image translation methods which use a color adjustment approach utilize a variety of intermediate color spaces. The great majority of these spaces are non-perceptual color spaces. This means that the colors within this type of color space are arranged on the axes in a way which is convenient to the operation of a machine, or some hardware device, but of questionable relevance to human perception. This creates problems when striving for accuracy. When all of the colors of the original image are brought into the target gamut through such an intermediate space, then the resulting colors will have changed from the original in an unpredictable fashion, in ways which do not agree with human judgment, and thus detrimental to the process of achieving accuracy. After having brought the colors of the original image into the gamut of the target medium through a non-perceptual space, there might remain a number of adjustments to be performed on the colors to bring the resulting image closer to what is considered accurate. With existing color image translation methods, many of these adjustment operations become necessary due to the limitations of the non-perceptual intermediate color spaces used, and the adjustments are designed to counter the distortions of this non-perceptual space. It is more desirable to be able to adjust the colors through an intermediate perceptual color space, one in which the colors have been arranged along the axes with the aid of human judgment for the purpose of visual consistency, such that the adjustment brings the colors to what the discerning human eye expects to see, as well as within the gamut of the target medium.
The use of a perceptual color space eliminates the need for the adjustment operations associated with non-perceptual color spaces. However, as previously described, there are problems inherent in the translation of colors from a source gamut to a target gamut which require remedial operations to achieve accuracy, regardless of the color space used. When adjusting a group of colors from one gamut to another very different gamut some of the colors may be cut off, or excluded, from the boundaries of the new gamut. For example, the original gamut may contain a broad spread of dark through light colors, and the new gamut may be lighter overall and contain a smaller spread of dark through light, defining a tighter color space. When the image is lightened or up-shifted into the new gamut, the former darkest colors fit easily into the darkest area of the new gamut, but the lightest colors no longer fit, since they are cut off by the tighter boundary of the new gamut. Remedial operations must be executed to make all of the colors fit into the new gamut and maintain their relative positions as closely as possible to achieve the greatest accuracy. We will call the process of executing such remedial operations relative repositioning or simply repositioning. Existing repositioning operations, such as color shifting, clipping, and scaling tend to be a patchwork of hit or miss estimations and/or difficult, error-prone observations all of which are very subjective. The subjective nature of the existing color translation accuracy evaluation processes results in a tedious, expensive, and often fruitless utilization of a technicians' time.
Therefore, it would be advantageous to employ a color image translation process that is able to objectively, rather than subjectively, evaluate the accuracy with which colors are repositioned during this translation process. More specifically, it would be advantageous if a color image translation process calculated a scalar quantity indicative of the accuracy with which colors are repositioned during the translation process.